3 ways modern master data management is driving better business outcomes

Master data management (MDM) provides a single source of truth for your data, ensuring uniformity, accuracy and stewardship across the enterprise
Cloud-native, graph-based and ML-driven MDM enhances efficiency and scalability
Data mastery is a prerequisite for organisational growth fuelled by data

For years, companies have poured resources into technology, tools, processes and talent to support master data management. The goal? To create a single, accurate source of truth for critical data across the organisation. However, in a fast-changing landscape marked by mergers, acquisitions and growing regulatory demands, implementing effective MDM is far from simple. The shift toward cloud-based applications and the high cost of maintaining outdated MDM systems is driving a transformation in how companies approach data mastery.

Traditional MDM solutions — especially those that are on-premise or cloud-hosted rather than truly cloud-native — rely heavily on manual data reconciliation and complex, disconnected systems, making them costly and difficult to manage at scale. This evolution has made modern, streamlined MDM solutions more essential than ever for companies aiming to stay agile and competitive. Cloud, big data and complementary technologies such as artificial intelligence (AI), machine learning (ML) and automation are converging in solutions that automate tasks to make data mastering faster and more affordable, even as data volumes explode.

Here are three ways that modern MDM can help companies achieve better business outcomes:

1. Streamlined collaboration and governance

Modern MDM makes data accessible beyond back-office functions. Today, sales, product and customer service teams also rely on accurate data to enhance the customer experience and speed up product launches. Legacy systems weren’t designed for this level of cross-functional engagement. However, modern MDM platforms enable seamless collaboration with features like contextual commenting, voting, guided data stewardship, and even chat integration, allowing users from different departments to access and manage data easily.

2. Graph-based search and data exploration

A graph-based, cloud-native MDM solution supports visual navigation of relationships between customers, products, contacts and other master and reference data. Companies experiencing high-volume growth, in particular, who need to scale and reduce latency, can benefit from the scalability of a cloud-native MDM platform.

For use cases requiring a real-time, 360-degree view of customers — including profiles, relationships, interactions and preferences — graph-powered solutions with an API-first architecture are ideal for supporting resource-intensive applications and customer data platforms. Customer preferences from various sources, such as websites and apps, or insights derived from telemetry from connected devices and the internet-of-things, can be stitched together with core customer data to create a 360-degree view. Legacy MDM solutions, on the other hand, often struggle to manage flexible and changing relationships between data in a sustainable manner.

3. Machine learning for data discovery and stewardship

Modern MDM solutions integrate machine learning (ML) to help improve employee experience and boost efficiency, which is increasingly relevant as data takes on a more central role in all aspects of business operations.

There are three primary areas where ML-powered MDM can demonstrate its advantages over legacy systems:

Automatic discovery: Source data analysis and ingestion is one of the most effort-intensive MDM activities. ML can streamline this process in a myriad of ways, automating data mapping and classification and enabling the discovery of data relationships through smart metadata mining and deep learning. ML-powered data protection and classification capabilities are especially helpful in finance and healthcare, which need to comply with a range of data privacy regulations.

You could, for instance, automatically classify data on tens of thousands of raw materials or finished products into standard or custom taxonomies — a possible game changer for consumer goods and industrial products companies. This drastic reduction of manual labour can improve efficiency and lower operations expenditures.
Self-healing: ML can help automate the process of identifying input data structure errors and recommend corrective action. Once data has been ingested, built-in anomaly detection and automated recommendations for remediation rules can help further enhance data accuracy.
Intelligent data stewardship: By using ML to automate match rule identification and provide recommendations, modern MDM solutions reduce the time needed for manual data handling. These self-learning algorithms, with human-in-the-loop oversight, can link and merge records, further enhancing data quality. This is particularly helpful for organisations managing large volumes of master data, as ML can automate workflows and offer guided data stewardship, significantly reducing the operational burden.

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Getting started with modern MDM

For companies with rudimentary or legacy MDM, or those that want to start with next-gen MDM, the first step is an assessment to identify business needs, data management gaps, and a governance model. A well-structured assessment can reveal the most impactful areas for MDM investment, particularly in complex environments affected by mergers or regulatory requirements.

The next step is creating an MDM strategy and execution plan. This is often incorporated into a Data Strategy as a key aspect of correctly managing data. This is especially crucial for organisations that have not yet embraced MDM and struggle to support operations and provide accurate data for external reporting and business planning.

Finally, MDM modernisation replaces legacy on-premises (or cloud) systems with cloud-native solutions that can scale as needed to support digital value creation — everything from e-commerce to automated back-office processes to accurate predictive analytics.

Embracing MDM today can transform your organisation into a more agile, data-driven enterprise — equipped to handle the demands of modern business and better positioned for future growth.

This is an abridged version of an article that originally appeared in PwC’s TechEffect. To learn more about how modern master data management may help you, contact Dave Littlewood, Chris Westhorpe and Matt Labovich.

Contact the authors

Dave Littlewood

Senior Manager, Advisory, PwC Australia

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Chris Westhorpe

Director, Advisory, AI, Data and Digital, Melbourne, PwC Australia

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Matt Labovich

Partner, Advisory, PwC United States

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3 ways modern master data management is driving better business outcomes

1. Streamlined collaboration and governance

2. Graph-based search and data exploration

3. Machine learning for data discovery and stewardship

Getting started with modern MDM

Contact the authors

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1. Streamlined collaboration and governance

2. Graph-based search and data exploration

3. Machine learning for data discovery and stewardship

Getting started with modern MDM

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